4 papers
MetaPusher: Meta Learning and Planning for Nonprehensile Manipulation of Unseen Objects with Rapid Online Adaption
Donghyung Lee, Seyedali Golestaneh, Jaskrit Singh +3
Manipulating previously unseen objects remains challenging, as their dynamics depend on latent physical properties, such as friction and mass distribution, that cannot be inferred…
AURA: Asymptotically Optimal Uncertainty-Robust Replanning Algorithm for Kinodynamic Systems
Seyedali Golestaneh, Zhuoyun Zhong, Donghyung Lee +1
Sampling-based motion planners offer a practical and scalable approach to kinodynamic motion planning, notably for high-dimensional, underactuated, or non-holonomic systems. Howeve…
Terminal Matters: Kinodynamic Planning with a Terminal Cost and Learned Uncertainty in Belief State-Cost Space
Zhuoyun Zhong, Seyedali Golestaneh, Constantinos Chamzas
In many real-world robotic tasks, robots must generate dynamically feasible motions that reliably reach desired goals even under uncertainty. Yet existing sampling-based kinodynami…
ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation
Zhuoyun Zhong, Seyedali Golestaneh, Constantinos Chamzas
Planning with learned dynamics models offers a promising approach toward versatile real-world manipulation, particularly in nonprehensile settings such as pushing or rolling, where…